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Statistical post-processing techniques are now widely used to correctsystematic biases and errors in calibration of ensemble forecasts obtained frommultiple runs of numerical weather prediction models. A standard approach isthe ensemble model output statistics (EMOS) method, a distributional regressionapproach where the forecast distribution is given by a single parametric lawwith parameters depending on the ensemble members. Choosing an appropriateparametric family for the weather variable of interest is a critical, however,often non-trivial task, and has been the focus of much recent research. In thisarticle, we assess the merits of combining predictive distributions frommultiple EMOS models based on different parametric families. In four casestudies with wind speed and precipitation forecasts from two ensembleprediction systems, we study whether state of the art forecast combinationmethods are able to improve forecast skill.

Combining predictive distributions for statistical post-processing of ensemble forecasts